Introduction

This vignette demonstrates how to calculate and visualize relatedness matrices for animal pedigrees using functions from the BGmisc and ggpedigree packages. We focus on Kluane Red Squirrel Project data, but the workflow generalizes to any pedigree dataset. The ggRelatednessMatrix function is a new addition to the ggpedigree package, which provides a flexible and visually appealing way to explore patterns of genetic and mitochondrial relatedness within families. This function is designed to work seamlessly with the ped2add and ped2mit functions from the BGmisc package, which calculate additive genetic and mitochondrial relatedness matrices, respectively.

We will use the new ggRelatednessMatrix function to create ggplot2-based heatmaps of relatedness matrices, inspired by the approaches in the original squirrel population analyses.

library(ggpedigree)
# Load the example data
data("redsquirrels")

Calculating Relatedness Matrices

Here we show how to compute additive genetic and mitochondrial relatedness matrices for a target family, as in the red squirrel analysis.

sumped <- summarizePedigrees(redsquirrels,
  famID = "famID",
  personID = "personID",
  nbiggest = 5
)


# Set target family for visualization
fam_filter <- sumped$biggest_families$famID[3]

# Filter for the largest family, recode sex if needed
ped_filtered <- redsquirrels %>%
  recodeSex(code_female = "F") %>%
  filter(famID == fam_filter)

# Calculate relatedness matrices
add_mat <- ped2add(ped_filtered, isChild_method = "partialparent", sparse = FALSE)
mit_mat <- ped2mit(ped_filtered, isChild_method = "partialparent", sparse = FALSE)

Visualizing Relatedness Matrices

The ggRelatednessMatrix function plots a relatedness matrix as a heatmap, with options for clustering and color customization. This approach mirrors the style of plots produced in the original red squirrel data analysis.

p_mit <- ggRelatednessMatrix(
  mit_mat,
  config = list(
    color_palette = c("white", "skyblue", "darkblue"),
    scale_midpoint = 0.55,
    cluster = TRUE,
    title = "Mitochondrial Relatedness",
    text_size = 6
  )
)
p_mit +
  labs(
    x = "Individuals",
    y = "Individuals"
  ) +
  theme(
    axis.text.y = element_blank(),
    axis.text.x = element_text(angle = -45, size = rel(.5))
  )

p_add <- ggRelatednessMatrix(
  add_mat,
  interactive = TRUE,
  config = list(
    color_palette = c("white", "orange", "red"),
    scale_midpoint = 0.55,
    cluster = TRUE,
    title = "Additive Genetic Relatedness",
    text_size = 5
  )
)
p_add

Customization Options

You can adjust clustering, color scales, labels, and more using the config list. For example, to turn off clustering:

p_add_noclust <- ggRelatednessMatrix(
  add_mat,
  config = list(cluster = FALSE, title = "Additive Relatedness (No Clustering)")
)
p_add_noclust

Comparison to Base R and Other Approaches

For comparison, here is how the same matrix would be plotted in corrplot, as in earlier squirrel analyses:

if (requireNamespace("corrplot", quietly = TRUE)) {
  corrplot::corrplot(
    as.matrix(add_mat),
    method = "color",
    type = "lower",
    col.lim = c(0, 1.25),
    is.corr = FALSE,
    title = "Additive Relatedness",
    order = "hclust",
    col = corrplot::COL1("Reds", 100),
    tl.pos = "l", tl.col = "black", tl.srt = 5, tl.cex = 0.2,
    mar = c(0, 0, 2, 0)
  )
}

Conclusions

The ggRelatednessMatrix function provides a flexible and visually appealing way to explore patterns of genetic and mitochondrial relatedness within families. This workflow integrates seamlessly with tools from BGmisc and ggpedigree, supporting reproducible, publication-quality visualization for quantitative genetic analysis.